Recovering from conflicts that emerge from eventually consistent operations

ABSTRACT

A method begins by detecting an inconsistency between a first version of an object at a first storage target within the DSN and a second version of the object at second storage target of the DSN. The method continues by accessing an operation log of the DSN to identify relevant entries regarding the object. The method continues by selecting an inconsistency resolution approach from a list of inconsistency resolution approaches based on a type of inconsistency between the first and second versions of the object and based on the relevant entries. When the selected inconsistency resolution approach is a multiple version storage option, the method continues by sending a first storage request to the first storage target to store the first version of the object and a second storage request to the second storage target to store the second version of the object.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.15/837,979, entitled “RECOVERING FROM CONFLICTS THAT EMERGE FROMEVENTUALLY CONSISTENT OPERATIONS”, filed Dec. 11, 2017, which is acontinuation-in-part of U.S. Utility application Ser. No. 14/794,723,entitled “CONSISTENCY BASED ACCESS OF DATA IN A DISPERSED STORAGENETWORK”, filed Jul. 8, 2015, issued as U.S. Pat. No. 10,049,120 on Aug.14, 2018, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/046,444, entitled “FACILITATING DATACONSISTENCY IN A DISPERSED STORAGE NETWORK,” filed Sep. 5, 2014, all ofwhich are hereby incorporated herein by reference in their entirety andmade part of the present U.S. Utility patent application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 10 is a flowchart illustrating an example of accessing a set ofstorage units in accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 12 is a flowchart illustrating an example of synchronizing storeddata in accordance with the present invention; and

FIG. 13 is a flowchart illustrating an example of recovering fromconflicts that may emerge from eventually consistent operations inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data (e.g., data 40) on behalf of computing device 14. Withthe use of dispersed storage error encoding and decoding, the DSN 10 istolerant of a significant number of storage unit failures (the number offailures is based on parameters of the dispersed storage error encodingfunction) without loss of data and without the need for a redundant orbackup copies of the data. Further, the DSN 10 stores data for anindefinite period of time without data loss and in a secure manner(e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes storage targets 1 and 2, network 24of FIG. 1 and a DS processing unit 96. The DS processing unit 96 may beimplemented by one or more of the computing devices 12-16, the DSNmanaging unit 18, and the integrity processing unit 20 of FIG. 1. TheDSN may be implemented utilizing the DSN of FIG. 1. The DSN functions toaccess data stored in the set of storage units 1-18. In an example ofoperation of the accessing of the data, the DS processing unit 96receives a data access request with consistency 90. The request includesa threshold number of storage targets indicator, where the set ofstorage units includes two or more storage targets (e.g., site 1, site2) and where each storage target includes at least a decode thresholdnumber of storage units. As such, the threshold number of storagetargets indicator indicates a minimum number of sites to be utilized inthe accessing of the data.

The request further includes at least one of a read data request or awrite data request. The threshold number indicates one of a read storagetargets threshold and a write storage target threshold, where the readstorage target threshold plus the write storage target threshold isgreater than a number of storage targets when providing strong dataconsistency. For example, the write storage target threshold is 2 whenthe read storage target threshold is 1 and the number of storage targetsis 2. As another example, the read storage target threshold is 1 whenthe write storage target threshold is 2 and the number of storagetargets is 2.

Having received the data access request with consistency 90, the DSprocessing unit 96 accesses a threshold number of storage targets toperform the data access request. For example, the DS processing unit 96accesses storage units of one storage target when the threshold numberis 1. As another example, the DS processing unit 96 accesses all storageunits of storage targets 1 and 2 when the threshold number is 2. Theaccessing includes issuing, via the network 24, either read slicerequests or write slice requests.

Having accessed the threshold number of storage targets, the DSprocessing unit 96 issues a data access response 92 to a requestingentity based on the accessing of the threshold number of storagetargets. For example, the DS processing unit 96 issues a data accessresponse 92 that includes a status indicator indicating a number ofstorage targets accessed when writing data. As another example, the DSprocessing unit 96 issues another data access response 92 that includesthe latest revision of data and the status indicator indicating thenumber of storage targets accessed when reading the data.

FIG. 10 is a flowchart illustrating an example of accessing a set ofstorage units. The method begins or continues at step 100 where aprocessing module (e.g., of a dispersed storage (DS) processing unit, ofa computing device, etc.) receives a data access request withconsistency, where the consistency includes a threshold number ofstorage targets indicator. A storage target may include of one of memorydevices, a storage unit(s), a set of storage units, a plurality of setsof storage units and a site. The method continues at step 102 where theprocessing module identifies two or more storage targets of a set ofstorage units associated with the data access request. The identifyingincludes at least one of interpreting system registry information,receiving an input, initiating a query, and interpreting a queryresponse.

The method continues at step 104 where the processing module selects athreshold number of storage targets of the two or more storage targetsbased on the threshold number of storage targets indicator. Theselecting includes at least one of interpreting an operation log toidentify a most recent revision, interpreting an error message, andinterpreting the system registry information, and receiving an input.

The method continues at step 106 where the processing module accessesthe selected threshold number of storage targets based on the dataaccess request. For example, when writing data, the processing moduleissues write slice requests to storage units associated with theselected storage targets and receives write slice responses. As anotherexample, when reading data, the processing module issues read slicerequests to the storage units associated with the selected storagetargets, receives read slice responses, identifies a most recentrevision, and decode slices associated with the most recent revision toreproduce the data. The accessing may further include identifying anumber of storage targets to utilize.

The method continues at step 108 where the processing module issues adata access response based on the accessing of the selected thresholdnumber of storage targets. For example, when writing the data, theprocessing module indicates the number of storage targets accessed. Asanother example, when reading the data, the processing module indicatesthe number of storage targets accessed and generates the data accessresponse to include the reproduce data.

FIG. 11 is a schematic block diagram of an embodiment of a dispersedstorage network (DSN). The DSN may be implemented utilizing the DSN ofFIG. 9. The DS processing units 1-2 further includes the dispersedstorage (DS) client module 34 of FIG. 1 and a synchronization module110. The synchronization module 110 may be implemented utilizing theprocessing module 50 of FIG. 2. The DSN functions to access data storedin the set of storage units and to synchronize stored data whentransitioning from the impaired mode to the strong consistency mode. Theimpaired mode is realized when less than all of the storage units areavailable and one or more of the write threshold number and the readthreshold number are adjusted for operations with less than a fullcomplement of the IDA width number of storage units. The strongconsistency mode is supported when all of the storage units areavailable and the read threshold plus the write threshold is greaterthan the IDA width number.

In an example of operation, the DS client module 34 of DS processingunit 1 initiates writing data to two or more sites (e.g., to two or morepartitions). For example, the DS client module 34 dispersed storageerror encodes a data segment to produce a set of encoded data slices1-18, sends, via the network 1, encoded data slices 1-9 to the site 1(e.g., a first partition) and attempts to send, via the network 2,encoded data slices 10-18 to the site 2 (e.g., a second partition).

Having sent the encoded data slices 10-18 to the site 2, the DS clientmodule 34 detects a write failure to site 2 (e.g., does not receivefavorable write slice responses within a time frame). Having detectedthe write failure, the DS client module 34 generates an operation logentry to indicate the write failure (e.g., storing slice names, astorage unit identifier, and a site identifier).

The synchronization module 110 initiates synchronization of storedrevisions of data across the two or more sites when the two or moresites are again available. For example, the synchronization module 110retrieves encoded data slices 1-9 corresponding to missing revisions ofencoded data slices associated with site 2, generates rebuilt encodeddata slices 10-18 from the retrieved encoded data slices, and stores therebuilt encoded data slices in the storage units of site 2 (e.g.,storage units 10-18).

To enable stabilization of the synchronization of the store data andfurther writing, the synchronization module 110 inhibits further writingof data by the DST client module 34 while a threshold number ofsynchronization operations remain open. When the threshold number ofsynchronization operations have completed, the synchronization module110 enables the further writing of the data.

FIG. 12 is a flowchart illustrating an example of synchronizing storeddata. The method begins or continues with steps 120 where a processingmodule (e.g., of a DS processing unit) initiates a storage operation tostore data as one or more sets of encoded data slices in at least twosubsets of a set of storage units. The method continues with step 122,where the processing module, when detecting a storage failure to onesubset of storage units, generates an operation log entry to indicate anincomplete storage operation.

The method continues at step 124 where the processing module initiatessynchronization of stored revisions of data across the at least twosubsets of storage units based on a plurality of operation log entries.The initiating includes detecting that the two or more subsets ofstorage units are available. The initiating further includes obtainingthe plurality of operation log entries, identifying missing encoded dataslices, rebuilding rebuilt encoded data slices corresponding to themissing encoded data slices, and storing the rebuilt encoded dataslices.

The method continues at step 126 where the processing module inhibitsfurther storing of more data while a threshold number of synchronizationoperations remain open. When a threshold number of synchronizationoperations have been completed, the method continues at step 128 wherethe processing module enables the further storing of the more data. Forexample, the processing module detects that the threshold number ofsynchronization operations have been completed and indicates that thestoring of the data is enabled. The method may loop back to step 120,where the processing module initiates the storage operation to storedata.

FIG. 13 is a flowchart illustrating an example of recovering fromconflicts that may emerge from eventually consistent operations. Forexample, the conflict may result from operations where an object (e.g.,an encoded data slice, a set of encoded data slices, a plurality of setsof encoded data slices, replicated copies of slices, etc.) is updated totwo different versions at two storage targets (e.g., two sets of storageunits). As another example, the conflict may result from operationswhere an object is deleted at one storage target and updated at anotherstorage target. As a further example, the conflict may result fromoperations where different meta-data fields are modified (e.g., updated,added differently) between different storage targets.

The method begins at step 130, where a computing device (e.g., thesynchronization module 110 of FIG. 11, a computing device 12-16 of FIG.1, etc.) of a dispersed storage network (DSN) detects an inconsistencybetween a first version of an object at a first storage target of theDSN and a second version of the object at second storage target of theDSN. The method continues at step 132, where the computing deviceaccesses an operation log of the DSN to identify relevant entriesregarding the object. For example, the operation log records requesteddata access operations for execution within the DSN.

The method continues at step 134, where the computing device selects aninconsistency resolution approach from a list of inconsistencyresolution approaches based on a type of inconsistency between the firstand second versions of the object and based on the relevant entries ofthe operation log. The list of inconsistency resolution approachesincludes a multiple version storage option, a most current versionstorage option, and a merged version storage option.

When the selected inconsistency resolution approach is the multipleversion storage option, the method continues at step 136, where thecomputing device sends a first storage request to the first storagetarget to store the first version of the object. The method continues atstep 138, where the computing device sends a second storage request tothe second storage target to store the second version of the object. Thecomputing device may also store respective version metadata of the firstand second versions, and any respective delete markers of the first andsecond versions in both the first and second storage targets.

When the selected inconsistency resolution approach is the most currentversion storage option, the method continues at step 140, where thecomputing device determines whether the first version of the object orthe second version of the object is more current based on the relevantentries of the operation log. When the first version of the object ismore current, the method continues to step 142, where the computingdevice determines an updating approach of the second version of theobject based on a type of the object. Alternatively, when the secondversion is more current the computing device performs steps 142-144 forthe first version.

In an example of operation, when the type of the object is an encodeddata slice of a set of encoded data slices, the first version of theobject is a first copy of the encoded data slice having a first revisionlevel, and the second version of the object is a second copy of theencoded data slice having a second revision level, the computing devicedetermines whether the first revision level is a more current revisionlevel of the encoded data slice than the second revision level. In oneexample, the computing device may compare a timestamp of the firstversion to a timestamp of the second version and use the more recentrespective timestamp as the more current revision level. In anotherexample, the computing device may compare a revision number of theencoded data slice having the first version to a revision number of theencoded data slice having the second version and use the version withthe higher respective revision number as the more current revisionlevel.

For example, when the first revision level is the more current revisionlevel of the encoded data slice, the computing device determines theupdating approach to include overwriting of the second version of theobject with the first version of the object by the second storagetarget. In this example, the first storage target is a first storageunit in a first set of storage units and the second storage target is afirst storage unit in a second set of storage units.

In another example of operation, when the type of the object is two ormore encoded data slices of a set of encoded data slices, the computingdevices determines whether the first revision level is a more currentrevision level for the set of encoded data slices than the secondrevision level. For example, the first version of the object is a firstencoded data slice of the two or more encoded data slices and the secondversion of the object is a second encoded data slice of the two or moreencoded data slices. The first encoded data slice has a first revisionlevel and the second encoded data slice has a second revision level.When the first revision level is the more current revision level of theset of encoded data slices, the computing device determines the updatingapproach includes rebuilding the second encoded data slice to have thefirst revision level. For example, the computing device obtains a decodethreshold number first revision level encoded data slices of the set ofencoded data slices to produce a rebuilt second encoded data slice.

In yet another example of operation, when the type of the object is anencoded data slice of a set of encoded data slices, the first version ofthe object is a first copy of the encoded data slice having a firstmetadata, and the second version of the object is a second copy of theencoded data slice having a second metadata, the computing devicedetermines whether the first metadata is more current metadata for theencoded data slice than the second metadata. When the first metadata isthe more current metadata for the encoded data slice than the secondmetadata, the computing device determines the updating approach includesoverwriting the second metadata with the first metadata by the secondstorage target.

In yet still another example of operation, when the type of the objectis two or more encoded data slices of a set of encoded data slices,wherein the first version of the object is a first encoded data slicehaving first metadata of the two or more encoded data slices and thesecond version of the object is a second encoded data slice havingsecond metadata of the two or more encoded data slices, the computingdevice determines whether the first metadata is more current metadatafor the encoded data slice than the second metadata. When the firstmetadata is the more current metadata for the encoded data slice thanthe second metadata, the computing device determines the updatingapproach to include updating the second metadata to be consistent withthe first metadata. The method continues at step 144, where thecomputing device implements the updating approach of the second versionof the object such that the updated second version of the object isconsistent with the first version of the object.

When the selected inconsistency resolution approach is the mergedversion storage option, the method continues at step 146, where thecomputing device determines a specific inconsistency between the firstversion of the object and the second version of the object. The methodcontinues at step 148, where the computing device determines whether thespecific inconsistency is compatible or incompatible. For example, thecomputing device determines whether changes to the metadata can bemerged (e.g., can the combination of distinct updated fields be combinedto form a new metadata object). When the specific inconsistency iscompatible, the method continues at step 150, where the computing deviceupdates the first and second versions of the object to include thespecific inconsistency. When the specific inconsistency is incompatible,the method continues back to step 140, where the computing deviceutilizes the most current version storage option (e.g., steps 140-144)to resolve the specific inconsistency.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprises: detecting, by a computingdevice of a dispersed storage network (DSN), an inconsistency between afirst version of an object at a first storage unit within the DSN and asecond version of the object at second storage unit of the DSN;accessing, by the computing device, an operation log of the DSN toidentify entries regarding the object, wherein the operation log recordsrequested data access operations for execution within the DSN;selecting, by the computing device, an inconsistency resolution approachfrom a list of inconsistency resolution approaches based on a type ofinconsistency between the first and second versions of the object andbased on the entries, wherein the list of inconsistency resolutionapproaches includes a multiple version storage option, a most currentversion storage option, and a merged version storage option; when theselected inconsistency resolution approach is the multiple versionstorage option: sending, by the computing device, a first storagerequest to the first storage unit to store the first version of theobject; and sending, by the computing device, a second storage requestto the second storage unit to store the second version of the object;when the selected inconsistency resolution approach is the mergedversion storage option: determining, by the computing device, a specificinconsistency between the first version of the object and the secondversion of the object; determining, by the computing device, whether thespecific inconsistency is compatible or incompatible; when the specificinconsistency is compatible: updating the first and second versions ofthe object to include the specific inconsistency; and when the specificinconsistency is incompatible: utilizing the most current versionstorage option to resolve the specific inconsistency; and when theselected inconsistency resolution approach is the most current versionstorage option: determining, by the computing device, whether the firstversion of the object or the second version of the object is morecurrent based on the entries of the operation log; when the firstversion of the object is more current, determining, by the computingdevice, determining an updating approach of the second version of theobject based on a type of the object, wherein determining the updatingapproach includes, when the type of the object is an encoded data sliceof a set of encoded data slices, the first version of the object is afirst copy of the encoded data slice having a first revision level, andthe second version of the object is a second copy of the encoded dataslice having a second revision level, determining whether the firstrevision level is a more current revision level of the encoded dataslice than the second revision level; and implementing, by the computingdevice, the updating approach of the second version of the object suchthat the updated second version of the object is consistent with thefirst version of the object.
 2. The method of claim 1, whereindetermining the updating further approach comprises: when the firstrevision level is the more current revision level of the encoded dataslice, determining the updating approach to include overwriting of thesecond version of the object with the first version of the object by thesecond storage unit, wherein the first storage unit is in a first set ofstorage units and the second storage unit is in a second set of storageunits.
 3. The method of claim 1, wherein determining the updatingapproach comprises: when the type of the object is two or more encodeddata slices of a set of encoded data slices, wherein the first versionof the object is a first encoded data slice of the two or more encodeddata slices and the second version of the object is a second encodeddata slice of the two or more encoded data slices, wherein the firstencoded data slice has a first revision level and the second encodeddata slice has a second revision level, determining whether the firstrevision level is a more current revision level for the set of encodeddata slices than the second revision level.
 4. The method of claim 3,wherein determining the updating approach further comprises: when thefirst revision level is the more current revision level of the set ofencoded data slices, determining the updating approach to includerebuilding the second encoded data slice to have the first revisionlevel, wherein the first storage unit is in a set of storage units andthe second storage unit is in the set of storage units.
 5. The method ofclaim 1, wherein determining the updating approach comprises: when thetype of the object is an encoded data slice of a set of encoded dataslices, the first version of the object is a first copy of the encodeddata slice having a first metadata, and the second version of the objectis a second copy of the encoded data slice having a second metadata,determining whether the first metadata is more current metadata for theencoded data slice than the second metadata; and when the first metadatais the more current metadata for the encoded data slice than the secondmetadata, determining the updating approach to include overwriting ofthe second metadata with the first metadata by the second storage unit.6. The method of claim 1, wherein determining the updating approachcomprises: when the type of the object is two or more encoded dataslices of a set of encoded data slices, wherein the first version of theobject is a first encoded data slice of the two or more encoded dataslices and the second version of the object is a second encoded dataslice of the two or more encoded data slices, wherein the first encodeddata slice has first metadata and the second encoded data slice hassecond metadata, determining whether the first metadata is more currentmetadata for the set of encoded data slices than the second metadata. 7.The method of claim 6, wherein determining the updating approach furthercomprises: when the first metadata is the more current metadata for theset of encoded data slices than the second metadata, determining theupdating approach to include updating the second metadata to beconsistent with the first metadata.
 8. A computing device of a dispersedstorage network (DSN), wherein the computing device comprises: memory;an interface; and a processing module operably coupled to the memory andthe interface, wherein the processing module is operable to: detect aninconsistency between a first version of an object at a first storageunit within the DSN and a second version of the object at second storageunit of the DSN; access an operation log of the DSN to identify entriesregarding the object, wherein the operation log records requested dataaccess operations for execution within the DSN; select an inconsistencyresolution approach from a list of inconsistency resolution approachesbased on a type of inconsistency between the first and second versionsof the object and based on the entries, wherein the list ofinconsistency resolution approaches includes a multiple version storageoption, a most current version storage option, and a merged versionstorage option; when the selected inconsistency resolution approach isthe multiple version storage option: send, via the interface, a firststorage request to the first storage unit to store the first version ofthe object; and send, via the interface, a second storage request to thesecond storage unit to store the second version of the object; when theselected inconsistency resolution approach is the merged version storageoption: determining, by the computing device, a specific inconsistencybetween the first version of the object and the second version of theobject; determining, by the computing device, whether the specificinconsistency is compatible or incompatible; when the specificinconsistency is compatible: updating the first and second versions ofthe object to include the specific inconsistency; and when the specificinconsistency is incompatible: utilizing the most current versionstorage option to resolve the specific inconsistency; and when theselected inconsistency resolution approach is the most current versionstorage option: determining, by the computing device, whether the firstversion of the object or the second version of the object is morecurrent based on the entries of the operation log; when the firstversion of the object is more current, determining, by the computingdevice, determining an updating approach of the second version of theobject based on a type of the object, wherein determining the updatingapproach includes, when the type of the object is an encoded data sliceof a set of encoded data slices, the first version of the object is afirst copy of the encoded data slice having a first revision level, andthe second version of the object is a second copy of the encoded dataslice having a second revision level, determining whether the firstrevision level is a more current revision level of the encoded dataslice than the second revision level; and implementing, by the computingdevice, the updating approach of the second version of the object suchthat the updated second version of the object is consistent with thefirst version of the object.
 9. The computing device of claim 8, whereinthe processing module is further operable to determine the updatingapproach by: when the first revision level is the more current revisionlevel of the encoded data slice, determining the updating approach toinclude overwriting of the second version of the object with the firstversion of the object by the second storage unit, wherein the firststorage unit is in a first set of storage units and the second storageunit is in a second set of storage units.
 10. The computing device ofclaim 8, wherein the processing module is further operable to determinethe updating approach by: when the type of the object is two or moreencoded data slices of a set of encoded data slices, wherein the firstversion of the object is a first encoded data slice of the two or moreencoded data slices and the second version of the object is a secondencoded data slice of the two or more encoded data slices, wherein thefirst encoded data slice has a first revision level and the secondencoded data slice has a second revision level, determining whether thefirst revision level is a more current revision level for the set ofencoded data slices than the second revision level.
 11. The computingdevice of claim 10, wherein the processing module is further operable todetermine the updating approach by: when the first revision level is themore current revision level of the set of encoded data slices,determining the updating approach to include rebuilding the secondencoded data slice to have the first revision level, wherein the firststorage unit is in a set of storage units and the second storage unit isin the set of storage units.
 12. The computing device of claim 8,wherein the processing module is further operable to determine theupdating approach by: when the type of the object is an encoded dataslice of a set of encoded data slices, the first version of the objectis a first copy of the encoded data slice having a first metadata, andthe second version of the object is a second copy of the encoded dataslice having a second metadata, determining whether the first metadatais more current metadata for the encoded data slice than the secondmetadata; and when the first metadata is the more current metadata forthe encoded data slice than the second metadata, determining theupdating approach to include overwriting of the second metadata with thefirst metadata by the second storage unit.
 13. The computing device ofclaim 8, wherein the processing module is further operable to determinethe updating approach by: when the type of the object is two or moreencoded data slices of a set of encoded data slices, wherein the firstversion of the object is a first encoded data slice of the two or moreencoded data slices and the second version of the object is a secondencoded data slice of the two or more encoded data slices, wherein thefirst encoded data slice has first metadata and the second encoded dataslice has second metadata, determining whether the first metadata ismore current metadata for the set of encoded data slices than the secondmetadata.
 14. The computing device of claim 13, wherein the processingmodule is further operable to determine the updating approach by: whenthe first metadata is the more current metadata for the set of encodeddata slices than the second metadata, determining the updating approachto include updating the second metadata to be consistent with the firstmetadata.
 15. A non-transitory computer readable storage mediumcomprises: at least one memory section that stores operationalinstructions that, when executed by one or more processing modules of acomputing devices of a dispersed storage network (DSN), causes thecomputing devices to perform operations including: detecting aninconsistency between a first version of an object at a first storageunit within the DSN and a second version of the object at second storageunit of the DSN; accessing an operation log of the DSN to identifyentries regarding the object, wherein the operation log recordsrequested data access operations for execution within the DSN; selectingan inconsistency resolution approach from a list of inconsistencyresolution approaches based on a type of inconsistency between the firstand second versions of the object and based on the entries, wherein thelist of inconsistency resolution approaches includes a multiple versionstorage option, a most current version storage option, and a mergedversion storage option; when the selected inconsistency resolutionapproach is the multiple version storage option: sending, by thecomputing device, a first storage request to the first storage unit tostore the first version of the object; and sending, by the computingdevice, a second storage request to the second storage unit to store thesecond version of the object; when the selected inconsistency resolutionapproach is the merged version storage option: determining, by thecomputing device, a specific inconsistency between the first version ofthe object and the second version of the object; determining, by thecomputing device, whether the specific inconsistency is compatible orincompatible; when the specific inconsistency is compatible: updatingthe first and second versions of the object to include the specificinconsistency; and when the specific inconsistency is incompatible:utilizing the most current version storage option to resolve thespecific inconsistency; and when the selected inconsistency resolutionapproach is the most current version storage option: determining, by thecomputing device, whether the first version of the object or the secondversion of the object is more current based on the entries of theoperation log; when the first version of the object is more current,determining, by the computing device, determining an updating approachof the second version of the object based on a type of the object,wherein determining the updating approach includes, when the type of theobject is an encoded data slice of a set of encoded data slices, thefirst version of the object is a first copy of the encoded data slicehaving a first revision level, and the second version of the object is asecond copy of the encoded data slice having a second revision level,determining whether the first revision level is a more current revisionlevel of the encoded data slice than the second revision level; andimplementing, by the computing device, the updating approach of thesecond version of the object such that the updated second version of theobject is consistent with the first version of the object.
 16. Thenon-transitory computer readable storage medium of claim 15, whereindetermining the updating further approach comprises: when the firstrevision level is the more current revision level of the encoded dataslice, determining the updating approach to include overwriting of thesecond version of the object with the first version of the object by thesecond storage unit, wherein the first storage unit is in a first set ofstorage units and the second storage unit is in a second set of storageunits.
 17. The non-transitory computer readable storage medium of claim15, wherein determining the updating approach comprises: when the typeof the object is two or more encoded data slices of a set of encodeddata slices, wherein the first version of the object is a first encodeddata slice of the two or more encoded data slices and the second versionof the object is a second encoded data slice of the two or more encodeddata slices, wherein the first encoded data slice has a first revisionlevel and the second encoded data slice has a second revision level,determining whether the first revision level is a more current revisionlevel for the set of encoded data slices than the second revision level.18. The non-transitory computer readable storage medium of claim 17,wherein determining the updating approach further comprises: when thefirst revision level is the more current revision level of the set ofencoded data slices, determining the updating approach to includerebuilding the second encoded data slice to have the first revisionlevel, wherein the first storage unit is in a set of storage units andthe second storage unit is in the set of storage units.
 19. Thenon-transitory computer readable storage medium of claim 15, whereindetermining the updating approach comprises: when the type of the objectis an encoded data slice of a set of encoded data slices, the firstversion of the object is a first copy of the encoded data slice having afirst metadata, and the second version of the object is a second copy ofthe encoded data slice having a second metadata, determining whether thefirst metadata is more current metadata for the encoded data slice thanthe second metadata; and when the first metadata is the more currentmetadata for the encoded data slice than the second metadata,determining the updating approach to include overwriting of the secondmetadata with the first metadata by the second storage unit.
 20. Thenon-transitory computer readable storage medium of claim 15, whereindetermining the updating approach comprises: when the type of the objectis two or more encoded data slices of a set of encoded data slices,wherein the first version of the object is a first encoded data slice ofthe two or more encoded data slices and the second version of the objectis a second encoded data slice of the two or more encoded data slices,wherein the first encoded data slice has first metadata and the secondencoded data slice has second metadata, determining whether the firstmetadata is more current metadata for the set of encoded data slicesthan the second metadata; and when the first metadata is the morecurrent metadata for the set of encoded data slices than the secondmetadata, determining the updating approach to include updating thesecond metadata to be consistent with the first metadata.